ThunderAgent: A Fast, Simple, and Program-Aware Agentic Inference System

Abstract

Large language models (LLMs) are now used to power complex multi-turn agentic workflows. Existing services run agentic inference by assembling isolated components: an LLM inference engine (e.g., vLLM) and a tool orchestrator (e.g., Kubernetes). Although agentic workflows involve multiple LLM and tool requests, existing services make scheduling decisions on a per-request basis, without end-to-end knowledge of the workflow. This leads to sub-optimal management of KV-caches and tool execution environments. To address the challenges, we propose \ouralg, an inference system that is aware of the end-to-end agent workflow. We abstract agentic workflows as \textit{LLM Programs}, enabling a unified view of heterogeneous resources, including KV caches, system states, and external tool assets such as disk memory and network ports. \ouralg introduces a program-aware scheduler and a tool resource manager designed to maximize KV cache hit rates, mitigate memory imbalances, and enable asynchronous environment preparation. Evaluations across coding, routing, and scientific discovery agents demonstrate that \ouralg achieves **1.5-3.6x** throughput improvements in serving, **1.8-3.9x** in RL rollout, and up to **4.2x** disk memory savings compared to state-of-the-art inference systems.